<?xml version="1.0" encoding="UTF-8" standalone="no"?><metadata xml:lang="en">
<Esri>
<CreaDate>20180724</CreaDate>
<CreaTime>07495000</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idAbs>This has been a 5 year process, where we explored, community science collection on mobile devices, bringing in partner data in many forms, multiple attemps at remote sensing, but the majoirty of thes points have been observed through ESRI and QGIS software, where staff added points with heads up digitizing using various imagery sources.</idAbs>
<searchKeys>
<keyword>The Intertwine Alliance</keyword>
<keyword>Oak Prairie Work Group</keyword>
<keyword>OPWG</keyword>
<keyword>RCS</keyword>
<keyword>regional conservation strategy</keyword>
<keyword>Quercus garryana</keyword>
<keyword>Oregon White Oak</keyword>
<keyword>Portland-Vancouver</keyword>
<keyword>Intertwine</keyword>
<keyword>grids</keyword>
<keyword>percentage</keyword>
<keyword>10 acre</keyword>
</searchKeys>
<idPurp>The RCS extent also has a 10 acre grid pattern for the entire extent.  The 2.5 acre grids nest within these.  So we summarized our data to 1) find the number of 2.5 acre grids within each 10 acre grid which oak was detected.  We also summed the number of acres our buffered points contained and created a percentage of Oaks within those 2.5 acre grids which contained oaks.  So if a 10 acre grid, contained three 2.5 acre grids and each had 0.5 acres of oak.  We would divided 1.5/7.5 and attribute the centroid of the 10 acre grid with 0.20 value or 20%.  We did this because loading the 290k oak points, or the 45k oak grid points had a very slow performance while zoomed out to view the Intertwine extent.   We hope this will allow the viewer to view data without exaggerating the abundance of the information relative to the landscape.</idPurp>
<idCredit>OakQuest 2018 release</idCredit>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
</metadata>